This document discusses data valuation and governance for Micropole. It recommends evaluating data usage internally for efficiency, revenue, strategic objectives, and customer value. Externally, data can be used in data marketplaces and by selling APIs or data. Effective data governance is still needed and should focus on quality, uniqueness, lifecycle, compliance, security and privacy. New data governance objectives include classification, transparency, and machine learning integration. The document recommends roadmapping data strategies by business outcomes and considering data governance applications to improve effectiveness using data valuation.

2. Data Valuation and Data
Governance for Micropole
Henry Peyret, Principal Analyst
December, 2015

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Agenda
› Data Valuation and Governance Effectiveness
› Data Governance Applications to improve Value
and Effectiveness
› Recommendations

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Data Valuation : Internal Data Usage
› Usual :
• For « efficiency » by Process mapping
› Non Usual :
• For money making by Revenue mapping
• For strategic support by Objectives mapping
• For non tangible benefit by
• For customers value by Values mapping

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Data Valuation : External Data Usage
› Data Economy
• OpenData
• External Data Sources Management
• Selling Data to aggregators (Axciom and
others)
› API Economy
• Selling Data
• Selling Services using your data

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Customer Journey
-2 days
-2 hours
Flight
+2 hours
+2 days
APIs from « mobile moments » …
Core processes
Reservation
Customer Loyalty
Flight System
Bagage Handling
Each « Mobile Moment » generates an API
• Book
Reservation
• Change
reservation
• Request
Upgrade
• Reserve Seat
• Check In
• Confirm departure time
• Request lounge acces
• Confirm gate location
• Get Duty Free
• Check arrival
time
• Order food
• Order
entertainment
• Set up Wi-Fi
?
• Arrange ground transportation
• Report and track lost luggage
• Confirm mileage points earned
?
• Fill out customer survey
• Book reward travel
• Verify upcoming reservations

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APIs from « mobile moments » … to « IoT
events »
Customer Journey
-2 days
-2 hours
Flight
+2 hours
+2 days
Core processes
Reservation
Customer Loyalty
Flight System
Bagage Handling
IoT Public Hubs
Mobile Moments IoT events+Insights
• Book Reservation
• Change reservation
• Request Upgrade
• Reserve Seat
Plane
OnBoarding Luggage
Passenger Presence
• Check In
• Confirm departure time
• Request lounge acces
• Confirm gate location
• Get Duty Free
• Check arrival time
• Order food
• Order entertainment
• Set up Wi-Fi
• Arrange ground transportation
• Report and track lost luggage
• Confirm mileage points earned
• Fill out customer survey
• Book reward travel
• Verify upcoming reservations
Beacon
OnBoarding Passenger
Airport/Lou
ngePresence
Taxi

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There is no data valuation without DG
› Data Governance is the corollary
to Data Valuation and vice versa
› But this is a different and evolving
DG :
• « Risks vs rewards »
• Caring about data usage… and not just
collection
• Managing policies within ecosystems

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What about data governance (1.0) valuation?
› Quality : efficiency in processes
› Uniqueness : efficiency in processes
› Lifecycle : costs savings about storage
› Compliance : risk avoidance and
regulation trust
› Security : risk avoidance and customer
trust
› Privacy : customer trust

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Data Governance 2.0 Motto: Protect And Serve
February 2014 “Data Governance Archetypes Shape The Focus And Fate Of Your Business”

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The changing nature of Data Governance Objectives
DG 1.0 objectives DG 2.0 Objectives
Quality 100% “Good enough” depending on
usage
Uniqueness Unicity Multi viewpoint
Lifecycle
(compliance)
Storage and availability at
best cost
From real time to historical at “no
cost”
Security Classification Zero trust
Metadata Preliminar
Access and storage
After
Build automation, management
and context => machine learning
Privacy Like security Offer transparency for
“customers”
Graphical
representation
-------- Learning interpretation of large
volume, 3D “navigation”
Algorithms -------- Applicability
Integration -------- Nature of integration influence
the data usage (real time, data
context)

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Agenda
› Data Governance Effectiveness Issues
› Data Governance Applications to improve
Value and Effectiveness
› Recommendations

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Semantic and/or Machine Learning Examples
› IBM Watson
› Semantic in Data Preparation : Trifacta, Paxata, Liaison
Technology Contivo, Alation, Informatica Live Data Map,
Dell Boomi
› Big Data ingestion : Tamr
› MDM : Pitney Bowes Spectrum (Graph DB), NexJ (Graph
Database)
› Semantic in analytics : Alteryx, Datameer,

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But Machine Learning Does Not Replace
Data Governance
› Govern your marchine learning : what
are the mechanisms to trust the machine
• What is the right set of documents?
• Have the results been reviewed by experts?
• Is the machine providing enough evidence and
transparency to justify the results?

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DG Stewardship Application completes
the DG landscape

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DG SA Vendors are forming an emerging
market
› BackOffice Associates
› Collibra
› Diaku
› Datum
› Global Data Excellence
› IBM

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Recommendations : DG is a journey
1. Road map your data quality and governance strategies by
business outcomes.
2. Plan for machine learning, semantic maps to replace traditional
DG tools but not replacing the need for DG
• Data variety and explosion of data engagement will outpace
manual development and stewardship of data
3. Consider DG Stewardship Applications to improve effectiveness
using Data Valuation

19. Thank you
forrester.com
Henry Peyret
+33 684829551
hpeyret@forrester.com
@hpeyret